A novel truncated approximation based algorithm for state estimation of discrete-time Markov jump linear systems

In this paper, state estimation problem for discrete-time Markov jump linear systems is considered. First, three equalities are proposed. Next, they are applied to the state estimation problem of considered systems so that a novel suboptimal algorithm in the sense of minimum mean-square error estimate is obtained where the computation and storage load of the suboptimal algorithm is not ever-increasing with the length of the noise observation sequence. The proposed algorithm and the suboptimal adaptive algorithm proposed in [1] are all based on a truncated approximation strategy. However, compared with the algorithm of [1], the proposed algorithm requires much less approximations. Computer simulations are carried out to evaluate the performance of the proposed algorithm.